What happens when AI is everywhere?

Three years ago, artificial intelligence was a science fiction cliché. Now it’s a humdrum part of our lives, like wifi in a coffee shop.

It even turned up in Theresa May’s Brexit white paper, which was published yesterday.

It wasn’t always thus. For about 60 years, artificial intelligence research went on in dusty university computer labs, where nobody paid it much attention.

Then in 2006, a breakthrough: Britain’s very own Dr Geoffrey Hinton invented a technique called “deep learning”.

Deep learning is a way of teaching computers to program themselves. It’s modelled on how the human brain works.

By combing lots of information with deep learning algorithms, computers were able to figure stuff out for themselves for the first time. At first they performed basic tasks like “recognise a cat”. Then moved onto figuring out what music a person might like to hear. Now AI can drive cars, sign the next Bruce Springsteen, and solve Brexit.

The invention of deep learning in 2006 caused the technology industry to perk up and pay attention to artificial intelligence research.

Before long Microsoft, Google and Facebook had signed up every PhD researcher with a passing interest in deep learning. For example Google paid £378m for a startup called DeepMind, mainly to get its hands on just 12 top drawer AI experts.

And that’s been the state of play for the last while. Big technology companies have hoovered up all the top talent.

Most of the experts work at the biggest technology companies. And the most exciting applications of deep learning, such as driverless cars, voice recognition, instant translation and medical diagnosis, have come from the big companies too.

AI was a big job…

There are three main reasons why AI research has, up to now, been concentrated at big companies. The first is to do with talent. Back in 2006 there were only a handful of deep learning experts on the planet. So the companies with the deepest pockets were able to sign them up.

The second reason, as cited by Marc Andreessen recently in a conversation with Tim B Lee of Vox , is the scale of the projects. Teaching a car to drive is a huge task, requiring huge resources. The Amazon Echo, a household helper which uses voice recognition, is the product of four years’ work by 1,500 engineers. Big problems require big budgets.

The third reason is data. Deep learning basically has two components: clever deep learning algorithms which show the computer how to learn from data; and lots of data. The big companies – Google and Facebook in particular – have more data than anyone. More data means better AI, all things being equal.

For those three reasons, many people assume AI is going to stay concentrated in giant technology companies. Where it’s hard for us to invest in it.

Well, happily, things are changing. The three forces which had kept AI inside big companies up to this point are starting to weaken. The industry is blooming.

But things are changing

According to Marc Andreessen, the venture capitalist, a few things are changing. The first is that there are just more AI experts around these days. Deep learning has been around for a decade, and lots of ambitious computer science types have realised that it’s a path to riches.

The second change is that the machine learning experts have cracked some big problems, such as teaching computers to drive and recognise objects in videos. With those breakthroughs “in the bank”, it’s getting easier to apply the techniques to new problems. So: you don’t need a small army of engineers to teach computers a new trick any more.

The third factor is that machine learning algorithms are getting more efficient. Where in 2011, an algorithm might have needed 12 million examples of a cat to figure out what a cat looks like; today it can do it with much fewer examples. That’s more or less the holy grail of machine learning – to teach a computer how to do something with relatively few examples. The human brain is good like that. Eventually it’s hoped computers will be able to pick things up quickly, too.

The last change is that AI is turning into a service. Big companies like Amazon are allowing third parties to embed Amazon’s AI into their products. So that a tiny startup with five employees can incorporate a world-class voice controlled AI into their rinky-dink alarm clock, or whatever it may be.

So what happens as this plays out? AI will get cheap and ubiquitous. It’ll be in the background, like computing or wireless internet. It’ll enrich all the other technologies we use. It’ll help us out with big jobs and small ones.

If you’re a subscriber to Technology Profits Confidential, none of this will be new to you.

I’ve already a couple of tiny startups making use of machine learning. If you’d like to take the next step and put a few quid into this technology, you can sign up for Technology Profits Confidential here.

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